Money demand stability: New evidence from transfer entropy
Bibliographic record
Abstract
This paper revisits the empirical relationship between interest rates and money demand from a novel perspective, i.e., information theory. Particularly, we utilize the model-free transfer entropy to quantify the flow of information from interest rates to monetary aggregates and present three findings. First, we document a hump-shaped informational link between interest rate and M1 monetary aggregate, with a rounded high point in the late 1980s and early 1990s. Second, we identify three structural shifts in the information transmission from interest rate to M1. The first two breakpoints occurred in the early 1980s and mid-1990s, likely as a response to the removal of Regulation Q and the introduction of sweep technology, respectively. The third shift took place during the relatively less-explored period of the early 2000s. Finally, we unravel a previously unreported pivotal distinction between the first two changepoints despite the apparent similarity in inducing money demand instability: the 1980s financial deregulations facilitate the transmission of information, whereas the 1990s financial reforms acted as an impediment to the information flow. Our results are robust to alternative entropy measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".